Partitioning Tests in Dose–Response Studies with Binary Outcomes

Author(s):  
Xiang Ling ◽  
Jason Hsu ◽  
Naitee Ting
2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 645-645
Author(s):  
Nicholas Resciniti ◽  
Alexander McLain ◽  
Anwar Merchant ◽  
Daniela Friedman ◽  
Matthew Lohman

Abstract Recent research has examined how the microbiome may influence cognitive outcomes; however, there is a paucity of research understanding how medication associated with dysbiosis may be associated with cognitive changes. This study used data from the Health and Retirement Study and the Prescription Drug Study subset for adults 51 and older (n=3,898). Continuous (0-27) and categorical (cognitively normal=12-27; cognitive impairment=7-11; and dementia=0-6) cognitive outcomes were used. Prescriptions utilized were proton pump inhibitors, antibiotics, selective serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotics, antihistamines, and a summed dose-response measure. Linear mixed models (LMM) and generalized linear mixed models (GLMM) were used for continuous and binary outcomes. For the LMM model, the main effect for those taking one medication was insignificant; however, the interaction with time showed a significant decrease over time (β: -0.07; 95% confidence interval (CI): -0.14, 0.01). The mean cognitive score was lower for those taking two or more medications (β: -1.48; 95% CI: -2.70, -0.25), although the interaction with time was insignificant. GLMM results showed those taking two or medications had odds that are 612% larger (odds ratio (OR): 7.12; 95% CI: 3.03, 16.71) of going from cognitively healthy to dementia but the interaction with time showed decreased odds over time (OR: 0.92; 95% CI 0.86, 0.97). For cognitive impairment, those who took two or more medications had odds that were 45% larger (OR: 1.45; 95% CI: 1.05, 2.00) of going from cognitively healthy to cognitively impaired. This study indicated a dose-response aspect to taking medications on cognitive outcomes.


2015 ◽  
Vol 69 (4) ◽  
pp. 374-398 ◽  
Author(s):  
E. Lorenz ◽  
C. Jenkner ◽  
W. Sauerbrei ◽  
H. Becher

2015 ◽  
Vol 8 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Andrea Discacciati ◽  
Alessio Crippa ◽  
Nicola Orsini

Biometrics ◽  
2004 ◽  
Vol 60 (1) ◽  
pp. 216-224 ◽  
Author(s):  
Karen E. Han ◽  
Paul J. Catalano ◽  
Pralay Senchaudhuri ◽  
Cyrus Mehta

1963 ◽  
Vol 45 (2) ◽  
pp. 209-214 ◽  
Author(s):  
Donald H. Hanscom ◽  
Armand Littman ◽  
Jack V. Pinto

1999 ◽  
Vol 1 ◽  
pp. S47-S47
Author(s):  
N PAYNE ◽  
R GROCOTTMASON ◽  
A IONESCU ◽  
B SJOBERG ◽  
I SANDBLOM ◽  
...  

Methodology ◽  
2008 ◽  
Vol 4 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Michael Höfler

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohen’s d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohen’s d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohen’s d to avoid the problems related to the latter. In any case, the absolute increment should complement d.


2002 ◽  
Author(s):  
N. B. Hansen ◽  
M. J. Lambert ◽  
E. M. Forman
Keyword(s):  

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